| config::Basic_configuration< Internal_configuration_type > | Contains all fields and methods for creating configuration template |
| neural_net::Basic_function< Value_type > | Basic class for defining functions |
| neural_net::Basic_generalized_training_weight< Value_type, Iteration_type, Network_function_type, Space_function_type, Network_topology, Space_topology, Index_type > | Template class for the generalized training functions |
| neural_net::Basic_neuron< Activation_function_type, Binary_operation_type > | Basic_neuron template class |
| neural_net::Basic_neuron< Activation_function_type(typename Binary_operation_type::result_type), Binary_operation_type(Value_type, Value_type) > | |
| neural_net::Basic_topology< Result_type, Index_type > | Basic class for topologies |
| neural_net::Basic_training_functional | Basic trainng functional |
| neural_net::Basic_wta_training_functional< Value_type, Parametres_type > | Class that is basic for Winner Takes All (WTA) algorithms |
| neural_net::Basic_wtm_training_functional< Value_type, Parameters_type, Iteration_type, Index_type, Topology_type > | Class that is basic for Winner Takes Most (WTM) algorithms |
| binary_function | |
| neural_net::Cauchy_function< Value_type, Scalar_type, Exponent_type > | Functor that computes Cauchy hat function |
| neural_net::City_topology< Index_type > | Topology for neural network that calculates distance between two neurons |
| neural_net::Classic_training_weight< Value_type, Iteration_type, Network_function_type, Space_function_type, Network_topology, Space_topology, Index_type > | Template class for the generalize training functions that calculates generalized weight classical way |
| operators::compose_f_gxy_gxy_t< OP1, OP2 > | Adaptator class compose_f_gxy_gxy_t |
| config::Configuration_container | Contains options dedicated for the program |
| neural_net::Constant_function< Value_type, Scalar_type > | Functor that computes constant function |
| data_parser::Data_parser< Data_container > | |
| distance::Basic_weak_distance_function< Value_type, Result_type > | |
| distance::Euclidean_distance_function< Value_type > | |
| neural_net::Experimental_training_weight< Value_type, Iteration_type, Network_function_type, Space_function_type, Network_topology, Space_topology, Index_type, Parameter_type > | Template class for the generalize training functions that calculates generalizeg weight in experimental way |
| neural_net::External_randomize | This class force not to use srand() in generation proces, therefore srand function is initialized externally |
| neural_net::Gauss_function< Value_type, Scalar_type, Exponent_type > | Functor that compute Gauss hat function |
| neural_net::Hexagonal_topology< Index_type > | Topology for neural network that calculates distance between two neurons |
| neural_net::Internal_randomize | This class force to use srand() in generation proces, therefore srand function is NOT initialized |
| neural_net::Linear_numeric_iterator< Value_type > | Class that defines bahavior of the linear numeric iterator. It begins with given value and iterate using given step |
| neural_net::Max_topology< Index_type > | Topology for neural network that calculates distance between two neurons |
| operators::Max_type< T_1, T_2 > | Template that estimates maximal of the mathematical types |
| operators::Max_type_private< S, T > | |
| operators::Max_type_private< T, T > | |
| neural_net::Basic_activation_function< Parameters_type, Value_type, Result_type > | |
| neural_net::Numeric_iterator< Value_type > | Class that defines bahavior of the numeric iterator |
| power | Helper class for calculating power |
| operators::power< T, E, false > | |
| operators::power< T, E, true > | |
| neural_net::Randomize_policy | |
| neural_net::Ranges< Container_type > | Class creates and claculates ranges of data |
| neural_net::Rectangular_container< Object_type > | |
| operators::Value_type< T > | |
| operators::Value_type< double > | |
| operators::Value_type< int > | |
| operators::Value_type< long > | |
| operators::Value_type< unsigned int > | |
| operators::Value_type< unsigned long > | |
| config::Version_struct | Composition of the numbers describing version number of the software e.g. 1.2.3 |
| distance::Weighted_euclidean_distance_function< Parameters_type, Value_type > | |
| neural_net::Wta_proportional_training_functional< Value_type, Parameters_type, Iteration_type > | Class that certain kind of WTA algorithm |
| neural_net::Wta_training_algorithm< Network_type, Value_type, Data_iterator_type, Training_functional_type, Numeric_iterator_type > | Class contains functionality for training kohonen network using WTA method |
| neural_net::Wtm_classical_training_functional< Value_type, Parameters_type, Iteration_type, Index_type, Generalized_training_weight_type > | Class that is basic for Winner Takes Most (WTM) algorithms |
| neural_net::Wtm_training_algorithm< Network_type, Value_type, Data_iterator_type, Training_functional_type, Index_type, Numeric_iterator_type > | Class contains functionality for training kohonen network using WTM method |
